Many consumers desire to order items or goods remotely, e.g., on-line, through the Internet, or using a specially designed application or app on a personal computer or mobile device, such as a tablet or cell phone. At least some known web hosting systems include search engines that allow consumers to enter search criteria and generate search results based on the consumer's search criteria. Known search engines may generate and display product lists to consumers via a website including products that are selected based on the search criteria.
Product classification is a key issue in e-commerce domains. A product is typically represented by metadata such as its title, image, color, weight and so on, and most of the product metadata is assigned manually by the seller. Once a product is uploaded to an e-commerce website, it is typically placed in multiple categories, in order to provide better user experience, efficient search, and assist computer recommendation systems. A few examples of categories are internal taxonomies (for business needs), public taxonomies (such as groceries and office equipment) and a product's shelf (a group of products that are presented together on an e-commerce web page). These categories vary with time in order to optimize search efficiency and to account for special events such as holidays, and big sport events. In order to address these needs, known e-commerce websites typically require human editors and/or human crowd sourcing platforms to classify products. However, due to the high amount of new products uploaded daily and the dynamic nature of the categories, machine learning solutions for product classification are very appealing as a mean to reduce time and economic costs of using human editors to assign product categories. Thus, precisely categorizing items emerges as a significant issue in e-commerce domains.
The present invention is aimed at one or more of the problems identified above.